In addition to the above model, for better model training results, we adopt the trainable offset for binarized activation [1]}, as shown in Fig. REF . This offset value can be merged with BN in the inference phase, which will not incur additional overhead for hardware implementation. Fig. REF  shows the trained offset value for each layer. Initial values are set to 0 for all layers. The figure shows that the appropriate offset is not the same for each layer, and the trainable offset can effectively preserve the extracted features.
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